End-to-end SARSA Reinforcement Learning Algorithm Implementation

Reinforcement learning SARSA algorithm visualization showing an agent learning optimal path through a grid environment

Overview

A comprehensive implementation of the SARSA (State-Action-Reward-State-Action) reinforcement learning algorithm in Python. This project demonstrates how to build an intelligent agent that learns optimal behavior through trial and error, with practical applications in robotics, game AI, and autonomous systems.

Key Skills Demonstrated

  • Reinforcement learning algorithm implementation
  • Python optimization techniques
  • Environment simulation and modeling
  • Policy and value function computation
  • Hyperparameter tuning
  • Data visualization for RL metrics

Project Impact & Applications

  • Demonstrated practical implementation of RL concepts
  • Visualized learning progress and policy evolution
  • Provided foundation for more advanced RL algorithms

Tools & Technologies

  • Python (NumPy, Matplotlib)
  • OpenAI Gym environments
  • Reinforcement learning frameworks
  • Data visualization libraries
  • Jupyter notebooks

Implementation Details

The project implements the complete SARSA algorithm with:

  • Epsilon-greedy exploration strategy
  • Q-value table initialization and updates
  • Learning rate and discount factor optimization
  • Episode-based training loop
  • Policy visualization and evaluation
  • Performance metric tracking

The implementation balances theoretical correctness with practical considerations, making it suitable for both educational purposes and real-world applications.